US11111868B2 - Method of exhaust gas management in internal combustion engines, corresponding system, engine, vehicle and computer program product - Google Patents
Method of exhaust gas management in internal combustion engines, corresponding system, engine, vehicle and computer program product Download PDFInfo
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- US11111868B2 US11111868B2 US16/058,770 US201816058770A US11111868B2 US 11111868 B2 US11111868 B2 US 11111868B2 US 201816058770 A US201816058770 A US 201816058770A US 11111868 B2 US11111868 B2 US 11111868B2
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02D—CONTROLLING COMBUSTION ENGINES
- F02D41/00—Electrical control of supply of combustible mixture or its constituents
- F02D41/0025—Controlling engines characterised by use of non-liquid fuels, pluralities of fuels, or non-fuel substances added to the combustible mixtures
- F02D41/0047—Controlling exhaust gas recirculation [EGR]
- F02D41/005—Controlling exhaust gas recirculation [EGR] according to engine operating conditions
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02D—CONTROLLING COMBUSTION ENGINES
- F02D41/00—Electrical control of supply of combustible mixture or its constituents
- F02D41/02—Circuit arrangements for generating control signals
- F02D41/14—Introducing closed-loop corrections
- F02D41/1401—Introducing closed-loop corrections characterised by the control or regulation method
- F02D41/1405—Neural network control
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02D—CONTROLLING COMBUSTION ENGINES
- F02D41/00—Electrical control of supply of combustible mixture or its constituents
- F02D41/02—Circuit arrangements for generating control signals
- F02D41/14—Introducing closed-loop corrections
- F02D41/1438—Introducing closed-loop corrections using means for determining characteristics of the combustion gases; Sensors therefor
- F02D41/1444—Introducing closed-loop corrections using means for determining characteristics of the combustion gases; Sensors therefor characterised by the characteristics of the combustion gases
- F02D41/1466—Introducing closed-loop corrections using means for determining characteristics of the combustion gases; Sensors therefor characterised by the characteristics of the combustion gases the characteristics being a soot concentration or content
- F02D41/1467—Introducing closed-loop corrections using means for determining characteristics of the combustion gases; Sensors therefor characterised by the characteristics of the combustion gases the characteristics being a soot concentration or content with determination means using an estimation
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02D—CONTROLLING COMBUSTION ENGINES
- F02D41/00—Electrical control of supply of combustible mixture or its constituents
- F02D41/0025—Controlling engines characterised by use of non-liquid fuels, pluralities of fuels, or non-fuel substances added to the combustible mixtures
- F02D41/0047—Controlling exhaust gas recirculation [EGR]
- F02D41/0065—Specific aspects of external EGR control
- F02D41/0072—Estimating, calculating or determining the EGR rate, amount or flow
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Definitions
- the description relates to exhaust gas management in internal combustion engines.
- One or more embodiments may apply to controlling pollutant emissions from motor vehicles.
- Diesel engines are widely used throughout Europe due to their higher thermal efficiency, which facilitates reducing fuel consumption and slowing down global warming. Diesel engines otherwise represent a major source of NOx and PM in urban areas.
- toxicity was found to increase as the particle size decreases. Fine particles may exhibit considerably higher toxicity per unit mass as compared to coarser particles. Furthermore, smaller particles are more likely to be inhaled and deposited in the respiratory tract and in the alveolar region by diffusion, causing respiratory diseases, inflammation, and damage to the lungs: see, e.g.: Donaldson, K., Li, X. Y., and MacNee, W.: “Ultrafine (Nanometer) Particle Mediated Lung Injury,” J. Aerosol Sci. 29:553-560.
- EURO 6 rules see, e.g., REGULATION (EC) No. 715/2007) are exemplary of these.
- Exhaust gas recirculation (EGR) technology is now extensively used in order to reduce NOx emissions: see, e.g., Zheng, M., Reader, G. T., and Hawley, J. G.: “Diesel engine exhaust gas recirculation: a review on advanced and novel concepts,” Energy Conversion & Management 45: 883:900, 2004.
- EGR exhaust gas recirculation
- EGR acts by reducing the in-cylinder temperature and the oxygen available in the cylinder, which may lead to an increase in particulate matter (PM).
- particulate particles are formed in the cylinder in the locally rich regions.
- Conventional engine control systems may adopt control maps to provide a desired value of EGR ratio as a function of the (instantaneous) values of certain engine parameters, such as angular speed and load, by pursuing a trade-off between NOx and smoke emissions. That is, in defining the engine control maps set by a manufacturer during calibration of an engine, the EGR percentage for each engine condition can be selected in such a way to achieve a certain balance between NOx and particulate emissions (taken as the total mass of particulate matter) by noting that an increase in the EGR percentage leads to a reduction in NOx emissions and an increase in particulate emissions. For that reason a sort of compromise is sought between these opposing factors.
- deposition of particles can occur on the wall of the EGR cooler and on the EGR valve. This changes the real EGR ratio with respect to a nominal desired one.
- One or more embodiments may relate to a corresponding system, a corresponding internal combustion engine as well as to a corresponding vehicle.
- One or more embodiments may relate to a computer program product loadable in the memory of at least one data processing circuit (e.g., a computer or controller) and including software code portions implementing the method of one or more embodiments.
- a computer program product is understood as being equivalent to reference to a non-transient computer-readable medium containing instructions for controlling a programmable data processing circuit in order to co-ordinate implementation of a method according to one or more embodiments.
- Reference to “at least one programmable data processing circuit” is intended to highlight the possibility for one or more embodiments to be implemented in modular and/or distributed form.
- One or more embodiments can provide a real-time control system configured for acting on the amount of re-circulated exhaust gas (EGR) in order to improve particle sizing in the exhaust gases from an internal combustion engine.
- EGR re-circulated exhaust gas
- One or more embodiments provide a control architecture which facilitates real-time monitoring of particulate particle characteristics by means of a neural network configured to operate as a “virtual” sensor.
- such a sensor can provide concentrations of particles with a specific aerodynamic diameter as an output.
- FIG. 1 is a diagram exemplary of particle size distributions measured for different EGR ratios
- FIGS. 2 to 4 are diagrams exemplary of particle number concentration as a function of EGR level measured for various engine speed and torque values
- FIG. 5 is a block diagram exemplary of a control architecture based on embodiments and adapted for use in a motor vehicle;
- FIG. 6 is a schematic representation of a “virtual” sensor based on a neural network
- FIG. 7 is a system diagram of the vehicle.
- references to “an embodiment” or “one embodiment” in the framework of the present description is intended to indicate that a particular configuration, structure, or characteristic described in relation to the embodiment is comprised in at least one embodiment.
- phrases such as “in an embodiment” or “in one embodiment” that may be present in one or more points of the present description do not necessarily refer to one and the same embodiment.
- particular conformations, structures, or characteristics may be combined in any adequate way in one or more embodiments.
- the prior art control strategies do not take into account the effect of the EGR ratio on particle size and number, which is regulated along with mass. That is, they do not take into account the effect of the EGR on the number of particles emitted and their size, while the number of particles is governed by standards such as Euro 6 standards and will expectedly become even more stringent in future regulations.
- One or more embodiments as exemplified herein aim at achieving an improved control of particulate matter (PM) emissions, also in terms of particle size and number.
- PM particulate matter
- One or more embodiments may adopt for that purpose a “virtual” sensor based on an artificial neural network (ANN) paradigm.
- ANN artificial neural network
- ANNs are data-processing systems somehow patterned after biological neural systems which have been already used in various areas of science and engineering where conventional modeling methods may be inadequate due, for instance, to the presence of highly nonlinear phenomena.
- ANNs possess the ability of “learning” what happens within the framework of a certain process without requiring any modeling of the underlying physical and chemical laws.
- predictions as provided by a well-trained ANN can be (much) faster than those obtainable via conventional simulation programs or mathematical models.
- One or more embodiments may involve a closed-loop control system that facilitates real-time managing the particle sizing in the exhaust gases of an internal combustion engine by using a neural network virtual sensor.
- Integrated combustion parameters (e.g., heat release, IMEP) were calculated by integrating the in-cylinder pressure data. The experiments were performed using diesel fuel commercially available in Italy at the time of filing the instant application with a sulphur content lower than 250 ppm.
- the engine E was provided with an exhaust gas recirculation (EGR) system as adapted to be installed in a motor vehicle V such as a motor car (see also FIG. 5 , to be discussed later).
- EGR exhaust gas recirculation
- An associated EGR circuit includes an electro-valve controlled by an electronic control unit (EGR controller as exemplified by 10 in FIG. 5 ).
- the electro-valve receives an electrical signal (duty cycle) and can produce a pressure reduction in an associated mechanical valve (EGR valve) between the outlet and the inlet pipes. With pressure reduction applied, the EGR valve facilitates the addition of a part of exhaust gases to the airflow.
- the whole process can be controlled by the electronic control unit 10 on the basis of characteristic look-up tables, e.g., as stored in a memory.
- the actual EGR percentage can be obtained by measuring the aspirated air ( ⁇ dot over (m) ⁇ ) with and without EGR and using the following equation:
- EGR ⁇ ⁇ % 1 - m . airwEGR m . airw / oEGR ⁇ p w / oEGR p wEGR ⁇ T wEGR T w / oEGR .
- NDIR non-dispersive infrared detectors
- An opacimeter was used to measure particulate mass concentration as a function of time.
- An opacimeter is a partial-flow system that measures the visible light attenuation (550 nm) from the exhaust gases.
- the instrument used had a 1-second resolution.
- the opacity percentage can be converted into particulate mass concentration by means of empirical relationships.
- Aerosol size distributions at the common rail diesel engine exhaust were evaluated by means of an electrical low pressure impactor (ELPI), which is an electrical impactor working at low pressure and capable of measuring real-time particle aerodynamic diameters in the range 7 nm-10 ⁇ m.
- ELPI electrical low pressure impactor
- a sample first passes through a unipolar positive polarity charger where the particles are electrically charged by ions produced in a corona discharge. After the charger, the particles pass on to a low-pressure impactor where they are classified according to their aerodynamic diameter.
- the stages of the impactor are electrically insulated and each stage is individually connected to an electrometer current amplifier.
- the charged particles collected in a specific impactor stage produce an electrical current, which is recorded by the respective electrometer channel.
- the current value of each channel is proportional to the number of particles collected, and thus to the particle concentration in the specific size range.
- the current values are converted to an aerodynamic size distribution using particle size dependent relationships which describe the properties of the charger and the impactor stages.
- FPS fine particle sampler
- the common rail diesel engine was run steady state at different operating conditions. In particular, engine angular speeds of 1000, 1500 and 2000 rpm were considered. The load was changed in the range 2-5 bar. EGR percentage was varied from 0 to 56% for each condition, without changing the pilot and main fuel injection strategy. In this way, the air-fuel ratio decreases with an increasing EGR due to the displacement of intake air.
- the ordinate scale in FIG. 1 is in dN/d Log Dp, namely the particle density expressed as the number of particles per cubic centimeter having a certain aerodynamic diameter D.
- particle formation is prompt in all the size ranges; however, the formation of larger particles is enhanced at lower temperatures, which boost growth processes such as coagulation and agglomeration, and with a lower presence of oxygen, which improves particle formation and reduces particle oxidation. This result appears to become more evident as the EGR increases.
- EGR level can be effectively used as a control variable in a system intended to facilitate reducing the amount of particles having smaller dimensions.
- One or more embodiments may involve a dedicated closed-loop control architecture capable of improving the characteristics of particulate emissions from internal combustion engines, e.g., by reducing the concentration of ultra-fine and nano-particles.
- aerosol size distribution as predicted by means of a neural network model can be used as the input value.
- the control variable is represented by the amount of exhaust gas (EGR) recirculated to the engine E.
- FIG. 5 An exemplary block diagram of such a control architecture is shown in FIG. 5 .
- the EGR controller 10 produces a correction term EGRcorr which is used to adjust (e.g., at a summation node 12 ) an EGR value EGRmap defined by engine control maps 14 in order to achieve a desired aerosol sizing.
- such a correction can occur, e.g., at a summation node 12 , so that an actual EGR value, EGRact is supplied to the engine E resulting from correction of EGRmap by means of EGRcorr.
- the EGR value EGRmap can be defined by the engine control maps 14 e.g., as a function of signals indicative of engine speed (signal S) and engine load (signal L). Sensing of those signals and processing thereof by the engine control maps 14 to produce the EGR value EGRmap (e.g., in conjunction with injection commands IC to be similarly supplied to the engine E) are conventional in the art, thus making it unnecessary to provide a more detailed description herein.
- the EGR controller 10 may be configured to provide the EGRcorr value on the basis of a particulate size distribution signal PSD predicted in real time by a particle size distribution estimator 16 including a neural network (e.g., an artificial neural network—ANN).
- a neural network e.g., an artificial neural network—ANN
- the EGR controller 10 may operate by increasing or reducing the EGRmap level (e.g., by adding or subtracting EGRcorr at the node 12 ) with the aim of obtaining a target value for the particulate size distribution signal PSD.
- a target value for PSD can be selected with the aim of reducing the concentration of ultra-fine and nano-particles. This may be intended to reduce their adverse effect on human health, but can also facilitate adapting the particle size to the characteristics of the particulate trap (not visible in figures) acting on the engine exhaust.
- an increase in exhaust gas recirculation may lead to an increase in smoke and particulate (PM) emissions, especially at high load.
- PM smoke and particulate
- the increase in PM emissions may be sharp. This can be taken into account in the calculation of the EGR correction term EGRcorr in the EGR controller 10 .
- the potential effect of EGR level correction on NOx emissions and engine performance may be taken into account.
- the neural network which implements the PSD model (as exemplified at 16 in FIGS. 5 and 6 ) provides, for a given engine operating condition, information about the characteristics of particulate emissions in terms of particle size distribution, thus operating as a virtual sensor.
- a virtual sensor as exemplified herein operates on the basis of the engine speed and load signal S and L (as sensed and provided—in manner known per se—to the control maps 14 ) plus a “real” value for the EGR, that is EGRreal (e.g., in percentage EGR %) as calculated in a EGR calculation block 18 coupled to the engine E.
- EGRreal e.g., in percentage EGR %
- the choice of seven diameter values and ii) the numerical figures given are purely exemplary and are not to be construed, even indirectly, in a limiting sense of the embodiments.
- the actual EGR ratio from the block 18 is input to the predictor 16 .
- This latter choice can take into account the fact that the amount of exhaust gas recirculated in the intake manifold may differ appreciably from the nominal value as output from engine control maps. This may be due to deposition of particulate on the walls of EGR valve and EGR cooler and to ageing effects of the EGR valve itself.
- a control architecture for real-time particle sizing management as exemplified in FIG. 5 includes a block 18 for extracting information about the actual EGR value, e.g., from a combustion pressure signal CPS from the engine E.
- the signal CPS can be provided by an in-cylinder pressure sensor, which is a sensor already present in many modern diesel engines.
- the AN-based predictor 16 can be devised by taking into account the paradigm of a learning machine trained and tested on experimental data.
- a feed-forward MLP multi-layer perceptron neural network with one hidden layer
- the learning model can be adopted as the learning model.
- the generalization capability of the neural network model can be improved by resorting to an automated regularization procedure applied during model training.
- Bayesian regularization as described, e.g., in
- a regularization procedure facilitates a smoother network response through a modification in the object function, e.g., by adding a residual term including the sum of squared weights of the network.
- a “best” model can be defined as the model with the highest a posteriori probability of being correct; that is:
- H ⁇ ) is the probability of the event conditioned by the correctness of the model.
- i 1, . . . , n ⁇ , where x i is the i-th input vector of the neural network model and y i , is the related i-th output vector used as data set for the model training.
- a normal Gaussian data distribution can be considered only if the model is correct.
- Table 1 reproduced below is exemplary of possible performance of a PSD (particulate size distribution) model in the estimation of particle size distribution, when applied to a testing set as discussed previously.
- Table 1 shows the absolute and relative mean square errors of the model in estimating the concentrations of particles with diameters of 8, 28, 54, 91, 154, 261 and 381 nm. The mean square error was found always to lie within the range 3-6% of the highest experimental data.
- Table 1 The (purely exemplary) results reported in Table 1 refer to a data set including 38 experimental points. The whole data set was divided in training and testing sets, including 28 and 10 experimental points, respectively. Training data were chosen in order to contain information spread evenly over the entire range of engine operative conditions, thus increasing the generalization capability of the neural network model. The number of neurons in the hidden layer was set equal to 5.
- neural network configurations can provide adequate performance in one or more embodiments.
- a method may include:
- sensing at least one sensing signal (see, e.g., S, L FIG. 5 , left hand side) indicative of operating conditions of an internal combustion engine (e.g., E),
- an exhaust gas recirculation control signal (e.g., EGRmap) for controlling exhaust gas recirculation in the internal combustion engine
- a particulate size distribution signal (e.g., PSD) indicative of the particulate size distribution in the exhaust of the internal combustion engine
- EGRmap exhaust gas recirculation control signal
- sensing the at least one sensing signal may include sensing a signal selected out of a speed signal (e.g., S), a load signal (e.g., L) or combinations thereof, and/or
- an exhaust gas recirculation control signal for controlling exhaust gas recirculation in the internal combustion engine may include applying engine control maps (e.g., 14 ) to the at least one sensing signal sensed.
- the particulate size distribution signal indicative of the particulate size distribution in the exhaust of the internal combustion engine may be produced as a function of at least one sensing signal (see, e.g., S, L, EGRreal on the right-hand side of FIG. 5 ) indicative of operating conditions of the internal combustion engine.
- One or more embodiments may contemplate producing the particulate size distribution signal indicative of the particulate size distribution in the exhaust of the internal combustion engine as a function of at least one sensing signal selected out of a speed signal (e.g., S), a load signal (e.g., L), an exhaust gas recirculation sensing signal (e.g., EGRreal) indicative of the actual amount of exhaust gas recirculation in the internal combustion engine or combinations thereof.
- a speed signal e.g., S
- a load signal e.g., L
- an exhaust gas recirculation sensing signal e.g., EGRreal
- producing the particulate size distribution signal indicative of the particulate size distribution in the exhaust of the internal combustion engine may include applying neural net processing to at least one sensing signal (e.g., S, L, EGRreal) indicative of operating conditions of the internal combustion engine.
- sensing signal e.g., S, L, EGRreal
- producing the particulate size distribution signal indicative of the particulate size distribution in the exhaust of the internal combustion engine may include targeting an, optionally adjustable, reference value (e.g., PSDref).
- PSDref optionally adjustable, reference value
- a system configured for operating with the method of one or more embodiments may include:
- an engine control module e.g., 14
- the exhaust gas recirculation control signal e.g., EGRmap
- a particulate size distribution estimator (e.g., 16 ) configured for producing the particulate size distribution signal indicative of the particulate size distribution in the exhaust of the internal combustion engine
- a correction node e.g., 12
- the exhaust gas recirculation control signal as a function of the particulate size distribution signal, thereby producing the corrected exhaust gas recirculation control signal (e.g., EGRact) and for controlling exhaust gas recirculation in the internal combustion engine as a function of the corrected exhaust gas recirculation control signal.
- the engine control module and particulate size distribution estimator could be discrete circuit elements of a circuit that includes the correction node.
- an internal combustion engine may be equipped with the system of one or more embodiments, the engine being coupled with:
- the engine control module in the system to provide the at least one sensing signal indicative of operating conditions of the internal combustion engine
- the correction node in the system to receive therefrom the corrected exhaust gas recirculation control signal.
- the engine may be further coupled (e.g., via CPS, 18 ) with the particulate size distribution estimator in the system to provide thereto an exhaust gas recirculation sensing signal indicative of the actual amount of exhaust gas recirculation in the internal combustion engine.
- One or more embodiments may include a motor vehicle (e.g., V) equipped with an internal combustion engine according to one or more embodiments.
- V a motor vehicle
- One or more embodiments may include a computer program product loadable in the memory of at least one computer (see, e.g., blocks 10 , 14 , 16 , 18 in FIG. 5 ) and including software code portions for performing the method of one or more embodiments.
- FIG. 7 Shown in FIG. 7 is a system diagram of the vehicle V.
- the vehicle V includes the engine E and an engine control system 20 that controls the engine.
- the engine control system includes a controller 22 , a speed sensor 24 that provides the speed signal S, a load sensor 26 that provides the load signal L, a pressor sensor 28 that provides the CPS signal, and a memory 30 .
- FIG. 7 depicts the sensors 24 - 28 separately from the engine E, those sensors would be integrated in the engine E.
- the controller 22 is typically implemented by a microprocessor that is controlled by software stored in the memory 30 .
- the memory 30 also stores the engine control maps 14 and the neural network that implements the particle size distribution estimator 16 .
- the controller 20 and memory 22 implement the control system shown in FIG. 5 based on the speed signal S from the speed sensor 24 , the load signal L from the load sensor 26 , and the CPS signal from the pressure sensor 28 .
- the controller 22 receives the CPS signal from the engine E and calculates the EGRreal value, thereby implementing the EGR calculation module 18 .
- the controller 22 uses the EGRreal value, speed S, and load L as inputs to the neural network that the controller 22 creates and stores in the memory 14 to implement the particle size distribution estimator 16 and determine the PSD. Further, the control determines the EGRcorr signal based on the PSD, thereby implementing the EGR controller 10 , corrects the EGRcorr signal based on the EGR value EGRmap obtained from the engine control maps 14 stored in the memory 30 to produce the actual EGR value EGRact that is applied to the engine, thereby implementing the correction node 12 .
Abstract
Description
-
- Hoard, J., Abarham, M., Styles, D. and Giuliano, J. M. et al.: “Diesel EGR Cooler Fouling,” SAE Technical paper 2008-01-2475, 2008, doi:10.4271/2008-01-2475;
- Zhang, F. and Nieuwstadt, M.: “Adaptive EGR Cooler Pressure Drop Estimation,” SAE Technical paper 2008-01-0624, 2008, doi: 10.4271/2008-01-0624;
- Maing, S., Lee, K. S., Song, S., Chun, K. M., et al.: “Simulation of the EGR Cooler Fouling Effect on NOx Emission of a Light Duty Diesel Engine,” KSAE07-F0035, pp. 214-220, 2007;
- Johnson, T. V., “Diesel Emission Control in Review,” SAE Technical paper 2008-01-0069, 2008, doi: 10.4271/2008-01-0069.
where θ is the parameter of the model Hθ used to describe a system, whose observed event is indicated with D.
D={(
where
where −r2 log(P(θ)) represents a kind of a priori information about the correct solution.
TABLE 1 | ||
Particle | Absolute mean square | Mean square |
diameter (nm) | error (cm−3) | error (%) |
8 | 5.0E+06 | 6 |
28 | 1.02E+07 | 4.4 |
54 | 2.36E+07 | 3.1 |
91 | 1.30E+07 | 4.3 |
154 | 2.14E+06 | 3.8 |
261 | 5.11E+05 | 3.3 |
381 | 2.51E+05 | 6 |
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